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Modeling Spatial and Spatio-temporal Co-occurrence Patterns. Mete Celik Spatial Database / Data Mining Group Department of Computer Science University of Minnesota mcelik@cs.umn.edu Advisor: Shashi Shekhar. MDCOP Motivating Example : Input. • Manpack stinger (2 Objects) • M1A1_tank - PowerPoint PPT Presentation
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Modeling Spatial and Spatio-temporal Co-occurrence Patterns
Mete Celik
Spatial Database / Data Mining Group
Department of Computer Science
University of Minnesota
mcelik@cs.umn.edu
Advisor: Shashi Shekhar
6
MDCOP Motivating Example : Input • Manpack stinger
(2 Objects)
• M1A1_tank
(3 Objects)
• M2_IFV
(3 Objects)
• Field_Marker
(6 Objects)
• T80_tank
(2 Objects)
• BRDM_AT5
(enemy) (1 Object)
• BMP1
(1 Object)
7
MDCOP Motivating Example : Output• Manpack stinger
(2 Objects)
• M1A1_tank
(3 Objects)
• M2_IFV
(3 Objects)
• Field_Marker
(6 Objects)
• T80_tank
(2 Objects)
• BRDM_AT5
(enemy) (1 Object)
• BMP1
(1 Object)
36
Real Dataset Description
Vehicle movement dataset 15 time slots, x and y coordinates are in meter 22 distinct vehicle types and their instances Minimum instance number 2, maximum instance number 78 Average instance number 19
Example Input from Spatio-temporal DatasetOutput: Spatio-temporal Co-occurrence Pattern (Manpack_stinger <M1, M2> , fire cover (e,g., Bradley tank <T1, T2>))
50
http://upload.wikimedia.org/wikipedia/en/c/cd/Original_distribution_of_wolf_subspecies.GIF
Ecology – zonal co-location pattern ICDM05 - Discovering co-evolving spatio-temporal event setshttp://www.argentinapurses.com/football/formLabel.gif
Game (tactics) – mixed-drove patternEmerging Infectious Diseases
Sustained emerging co-occurrence patterns
5. Periodic co-occurrence patterns
6. Spatio-temporal cascade patterns
. . .
2. Co-occurrence patterns of moving objects Flock pattern, mixed-drove pattern, follow pattern, moving clusters, etc.
Spatio-temporal Co-occurrence Pattern Taxonomy
1. Spatial co-location Global and zonal co-location patterns, etc.
ICDM07 – Zonal Co-location Pattern Mining ICDM05 – Joinless Approach for Co-location Pattern Mining
TKDE08 and ICDM06 - Mixed-Drove Spatio-Temporal Co-occurrence
Pattern Mining ICDE-STDM07 - Mining At Most Top-K% Mixed-drove Spatio-temporal
Co-occurrence Patterns
3. Emerging or vanishing co-occurrence patterns Emerging pattern: Interest measure getting stronger by the time Vanishing pattern: Interest measure getting weaker by the time
ICTAI06 - Sustained Emerging Spatio-temporal Co-occurrence Pattern Mining
4. Co-evolving patterns
51
Chapter 2- Zonal Co-location Pattern Discovery
Zones 2,4 Zone 3
3
1 2
4
Given: different object types of spatial events and zone boundaries
Find : Co-located subset of event types specific to zones
Method: A novel algorithm by using an indexing structure.
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Chapter 4 - Sustained Emerging ST Co-occurrence Pattern Discovery
Given: A set P of Boolean ST object-types over a common ST framework
Find: Sustained emerging spatio-temporal co-occurrence patterns whose prevalence measure increase over time.
Method: Developing novel algorithms by defining monotonic interest measures.
53
Future Work – Short Term
1. Spatial co-location Interest measure: participation index Global and zonal co-location patterns, etc.
2. Co-occurrence patterns of moving objects Flock pattern, mixed-drove pattern, follow
pattern, cross pattern, moving clusters, etc.
3. Emerging or vanishing co-occurrence patterns Emerging pattern: Interest measure getting
stronger by the time Vanishing pattern: Interest measure getting
weaker by the time
4. Co-evolving patterns
5. Periodic co-occurrence patterns6. Spatio-temporal cascade patterns
• Efficient methods
• Comparison of int. measures with statistical int. measures
54
Future Work – Long Term
Spatial and Spatio-temporal Pattern Mining Design Crime Analysis, GIS, Epidemiology
Challenges discovering patterns and anomalies from enormous frequently updated
spatial and spatio-temporal datasets,
developing an ontological framework for spatial and spatio-temporal analysis,
integrating spatial and spatio-temporal data from multiple agencies, distributed data, and multi-scale data
55
Acknowledgements
Adviser: Prof. Shashi Shekhar
Committee: Prof. Jaideep Srivastava, Prof. Arindam Banerjee, and Prof. Sudipto Banerjee
Spatial Databases and Data Mining Group
TEC collaborators: James P. Rogers, James A. Shine
Dept. of Computer Science
56
References
[1] J. Gudmundsson, M. v. Kreveld, and B. Speckmann, Efficient Detection of Motion Patterns in Spatio-Temporal Data Sets, ACM-GIS,250-257, 2004.
[2] P. Laube and S. Imfeld, Analyzing relative motion within groups of trackable moving point objects, in In GIScience, number 2478 in Lecture notes in Computer Science. Berlin: Springer, pp. 132-144, 2002.
[3] P. Kalnis, N. Mamoulis, and S. Bakiras, On Discovering Moving Clusters in Spatio-temporal Data, 9th Int'l Symp. on Spatial and Temporal Databases (SSTD), Angra dos Reis, Brazil, 2005.
[4] Y. Huang, S. Shekhar, and H. Xiong, Discovering Co-location Patterns from Spatial Datasets: A General Approach, IEEE Trans. on Knowledge and Data Eng. (TKDE), vol. 16(12), pp. 1472-1485, 2004.
[5] M. Hadjieleftheriou, G. Kollios, P. Bakalov, and V. J. Tsotras, Complex Spatio-Temporal Pattern Queries, VLDB, pp. 877-888, 2005.
[6] C. du Mouza and P. Rigaux, Mobility Patterns, GeoInformatica, 9(4), 297-319, 2005.
[7] J. S. Yoo and S. Shekhar, A Join-less Approach for Mining Spatial Co-location Patterns, IEEE Trans. on Knowledge and Data Eng. (TKDE), Vol.18, No.10, 2006.
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